% Key papers for Expressive equivariant message passing (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2022_recipe_65847,
  title = {{Recipe for a General, Powerful, Scalable Graph Transformer}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54958},
}

@inproceedings{neurips2019_diffusion_58137,
  title = {{Diffusion Improves Graph Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14273},
}

@inproceedings{cvpr2020_dynamic_15448,
  title = {{Dynamic Graph Message Passing Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{icml2021_e_39278,
  title = {{E(n) Equivariant Graph Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9279},
}

@inproceedings{iclr2019_invariant_29102,
  title = {{Invariant and Equivariant Graph Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/764},
}

@inproceedings{iclr2020_global_29264,
  title = {{Global Relational Models of Source Code}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=B1lnbRNtwr},
}

@inproceedings{cvpr2023_hood_21121,
  title = {{HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22073},
}

@inproceedings{neurips2022_nodeformer_66170,
  title = {{NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54272},
}

@inproceedings{cvpr2023_edge_20882,
  title = {{Edge-Aware Regional Message Passing Controller for Image Forgery Localization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/23101},
}

@inproceedings{iclr2021_retrieval_30778,
  title = {{Retrieval-Augmented Generation for Code Summarization via Hybrid GNN}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3330},
}

@inproceedings{neurips2022_pure_66283,
  title = {{Pure Transformers are Powerful Graph Learners}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54611},
}

@inproceedings{icml2020_generalization_38244,
  title = {{Generalization and Representational Limits of Graph Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6249},
}

@inproceedings{neurips2020_natural_60381,
  title = {{Natural Graph Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17375},
}

@inproceedings{aaai2024_provably_7023,
  title = {{Provably Powerful Graph Neural Networks for Directed Multigraphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29069},
}

@inproceedings{neurips2019_hyper_58378,
  title = {{Hyper-Graph-Network Decoders for Block Codes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13428},
}

@inproceedings{neurips2020_building_59489,
  title = {{Building powerful and equivariant graph neural networks with structural message-passing}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17825},
}

@inproceedings{aaai2019_learning_101639,
  title = {{Learning Multi-Task Communication with Message Passing for Sequence Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2019},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/4346},
}

@inproceedings{neurips2021_dropgnn_62838,
  title = {{DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26554},
}

@inproceedings{iclr2023_cktgnn_32466,
  title = {{CktGNN: Circuit Graph Neural Network for Electronic Design Automation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/10780},
}

@inproceedings{neurips2021_rim_61628,
  title = {{RIM: Reliable Influence-based Active Learning on Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28121},
}

@inproceedings{iclr2024_partitioning_35452,
  title = {{Partitioning Message Passing for Graph Fraud Detection}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17621},
}

@inproceedings{neurips2024_boosting_72548,
  title = {{Boosting Graph Pooling with Persistent Homology}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94823},
}

@inproceedings{cvpr2024_flowerformer_23492,
  title = {{FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29874},
}

@inproceedings{iclr2024_from_33456,
  title = {{From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19619},
}

@inproceedings{aaai2025_on_10169,
  title = {{On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33858},
}

@inproceedings{icml2024_cooperative_44713,
  title = {{Cooperative Graph Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33739},
}

@inproceedings{aaai2026_tab_107093,
  title = {{Tab-PET: Graph-Based Positional Encodings for Tabular Transformers}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39445},
}

@inproceedings{iclr2024_on_35608,
  title = {{On the Stability of Expressive Positional Encodings for Graphs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17465},
}

@inproceedings{neurips2025_the_75322,
  title = {{The Underappreciated Power of Vision Models for Graph Structural Understanding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=6qBK9ePmtM},
}

@inproceedings{neurips2025_on_76794,
  title = {{On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=N4cyRMuLyl},
}

@inproceedings{aaai2026_connectivity_106803,
  title = {{Connectivity-Guided Sparsification of 2-FWL GNNs: Preserving Full Expressivity with Improved Efficiency}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39110},
}

@inproceedings{aaai2026_dual_106965,
  title = {{Dual Mamba for Node-Specific Representation Learning: Tackling Over-Smoothing with Selective State Space Modeling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39317},
}

@inproceedings{aaai2026_learning_107438,
  title = {{Learning Network Dismantling Without Handcrafted Inputs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39790},
}

@inproceedings{iclr2024_distinguished_33826,
  title = {{Distinguished In Uniform: Self-Attention Vs. Virtual Nodes}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19249},
}

@inproceedings{iclr2024_simplicial_33946,
  title = {{Simplicial Representation Learning with Neural \$k\$-Forms}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19129},
}

@inproceedings{iclr2024_learning_34348,
  title = {{Learning From Simplicial Data Based on Random Walks and 1D Convolutions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18727},
}

@inproceedings{icml2024_recurrent_43484,
  title = {{Recurrent Distance Filtering for Graph Representation Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34968},
}

@inproceedings{icml2024_subgraphormer_43545,
  title = {{Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34907},
}

@inproceedings{icml2024_generalization_43612,
  title = {{Generalization Error of Graph Neural Networks in the Mean-field Regime}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34840},
}